README.md

---
license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical
- fundus
- retina
- glaucoma
- optic-disc
- optic-cup
size_categories:
- 1K<n<10K
---

# Chaksu

Optic **disc** and optic **cup** segmentation in colour fundus photographs, for
glaucoma assessment. Mirror of the Chákṣu database for the MedOtter benchmark suite.

| | |
|---|---|
| Modality | Colour fundus photography (2D RGB) |
| Task | Optic disc + optic cup segmentation (also carries a glaucoma decision label) |
| Images | **1,345** — train 1,009 / test 336 |
| Cameras | Remidio FoP 2448×3264 (1,074) · Bosch 1920×1440 (145) · Forus 3Nethra 2048×1536 (126) |
| Ground truth | **STAPLE** fusion of 5 expert annotators |
| License | CC BY 4.0 |

## Ground truth

Five ophthalmologists annotated every image. The source release ships four fusions
of those annotations; the paper designates **STAPLE** as the gold standard, having
the highest agreement with the individual experts:

| Fusion | OD Dice | Role here |
|---|---|---|
| **STAPLE** | **0.9764** | **primary** → `mask`, `disc_mask`, `cup_mask` |
| Majority (≥3 of 5) | 0.9732 | secondary → `*_mask_majority` |
| Median | 0.9706 | secondary → `*_mask_median` |
| Mean (all 5 agree) | 0.9503 | not mirrored (lowest agreement) |

`mask` is a disjoint label map at native image resolution:

| value | class |
|---|---|
| 0 | background |
| 1 | neuroretinal rim (disc minus cup) |
| 2 | optic cup |

so `disc = mask > 0` and `cup = mask == 2`.

STAPLE masks are stored anti-aliased upstream; they are binarised at >127. In a
small number of images the cup exceeded the disc by a handful of edge pixels, so
`disc_mask` is stored as *disc ∪ cup* to guarantee cup ⊆ disc.

## Columns

| column | notes |
|---|---|
| `image` | original fundus photograph, native resolution, unmodified bytes |
| `mask` | disjoint 0/1/2 label map (STAPLE) |
| `disc_mask`, `cup_mask` | binary 0/255 (STAPLE) |
| `disc_mask_majority`, `cup_mask_majority` | binary 0/255 (Majority fusion) |
| `disc_mask_median`, `cup_mask_median` | binary 0/255 (Median fusion) |
| `image_id`, `camera`, `split`, `height`, `width` | identifiers / geometry |
| `glaucoma_decision` | majority vote of 5 experts: `NORMAL` / `GLAUCOMA SUSPECT` |
| `decision_expert_1..5` | per-expert decision (whitespace/spelling normalised) |
| `disc_area`, `cup_area`, `rim_area`, `cup_height`, `cup_width`, `disc_height`, `disc_width` | pixel measurements from the source CSVs |
| `acdr`, `vcdr`, `hcdr` | area / vertical / horizontal cup-to-disc ratio |

## Scope of this mirror

This is a **subset** of the full figshare release, restricted to what a
segmentation benchmark needs. Present: tier 1.0 (original images), tier 5.0
STAPLE/Majority/Median fusions, tier 6.0 metrics and decisions. **Not mirrored:**
tier 2.0 (expert contour markups), tier 3.0 (per-expert binary masks — note
Expert 3 has no Bosch folder), tier 4.0 (OD+OC fusion images), and the Mean and
Overlay variants of tier 5.0. Those total ~200 GB of mostly uncompressed TIFF and
are available from the original figshare deposit.

## Caveats

- **Patient overlap across the official split.** Only the Bosch subset carries
  patient IDs, and at least one patient straddles the boundary (`P10_Image1` in
  train, `P10_Image2` in test). Forus and Remidio splits are sequential cuts by
  capture order, not patient-aware, so fellow-eye pairs may also straddle it.
  The official split is preserved as published.
- Source tier-6 CSVs are partly duplicated across the train and test folders
  (the Bosch CSV is dataset-wide in both); they are merged by `(camera, image_id)`.
- Expert decision strings upstream include a misspelling (`GLAUCOMA  SUSUPECT`);
  these are normalised to `GLAUCOMA SUSPECT`.
- No overlap with REFUGE, DRISHTI-GS, RIM-ONE, ORIGA, PAPILA, IDRiD, DRIVE or
  RAVIR — Chaksu was newly acquired at KMC/MIT Manipal.

## Citation

```bibtex
@article{kumar2023chaksu,
  title   = {Chaksu: A glaucoma specific fundus image database},
  author  = {Kumar, J. R. Harish and Seelamantula, Chandra Sekhar and
             Gagan, J. H. and Kamath, Yogish S. and Kuzhuppilly, Neetha I. R. and
             Vivekanand, U. and Gupta, Preeti and Patil, Shilpa},
  journal = {Scientific Data},
  volume  = {10}, number = {1}, pages = {70}, year = {2023},
  doi     = {10.1038/s41597-023-01943-4}
}
```

Original data: <https://doi.org/10.6084/m9.figshare.20123135> (CC BY 4.0).